Advancements in the field of medical imaging techniques and associated sensors and/or devices have made it possible to localize the internal organs of a human or animal body, for clinical analysis and medical purposes. In conventional systems, different medical imaging modalities, such as a Computed Tomography (CT) image, a Magnetic Resonance Imaging (MRI), an X-ray and the like, are used in internal organ localization. Typically, physicians and radiologists may have to manually delineate an internal organ region slice by slice from the CT image that may be tedious and undesirable.
In certain scenarios, an atlas-based method is used to localize and segment the internal organ, for example, a liver from the CT image. An atlas image is a specific model generated from a large dataset of a plurality of CT images. The physicians and radiologists delineate a region of interest of an internal organ of interest by segmenting the internal organ of interest slice by slice from each of the plurality of CT images to generate the atlas image. The generated atlas image is further utilized to localize and segment the internal organ of interest in CT images of a given user. The localization of the internal organ of interest from the atlas-based method may be inaccurate and the percentage of error for each patient may be different as the spatial position, size, shape and appearance of the internal organ of interest may be different for each user. In certain scenarios, the spatial position, size, shape and appearance of the internal organ of the patient may look different when observed by a surgical camera, such as a laparoscope, from normal due to an infection or a disease. Thus, the localization of the internal organ of based on the atlas image may be inaccurate. Furthermore, generation of the atlas image from the large dataset by the radiologists may be tedious and undesirable.
In certain other scenarios, a classification-based method may be utilized to localize the internal organ from the CT image. The classification-based method uses training data to extract a set of image features, such as region mean intensity, variance, location, histogram and contextual features, to train a classifier by which probability map for the internal organ is generated. The internal organ of interest is localized based on the generated probability map which may not be accurate as the extracted set of image features may be different for different users.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.